Marketing Productivity Blog

The following is from the March 2008 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here.

Q: I found your web site through a Google search on “incremental business” and “gift cards”– but, then again, you probably already know that.  Didn’t find anything specific to “gift cards” on your site, but I’m wondering your opinion of them as CRM tools?

A:  Well, as I’m sure you know, they can be used in lots of different ways.  From an incremental perspective, some approaches are not so profitable and others quite profitable.

Q:  My quest at the moment is to try to help a client “prove” the incrementality of the business produced through gift cards and what avenues of sale might be more profitable than others.  Is there a correlation to this  quest and information in your book, i.e., marketing model versus financial model, success based not on “response” but on “profit” of the program?

A:  That’s pretty much what the entire book is about - how do you set up, test, and measure these kinds of ideas? The marketing model IS the financial model, from my perspective.

Q:  By the way, I’m not the only one pondering this in my industry.  Might be a topic you want to explore in your future endeavors which would be of interest to a host of us.

A:  OK, let’s do some of that.  My answers to e-mails like this frequently become newsletter or blog material (and I won’t reveal who asked the question), so perhaps I can both help you and get some writing done!

From a broad perspective, I don’t think there is any way to know the answer to your question without testing using control groups, and the ability of the client to execute on that idea can be limited if they are a mass retailer.  Plus, you have not told me anything about your client or how they promote the cards.  So we probably have to speculate based on what we know about behavior in general from other sources - loyalty programs and so forth.

In my mind, there’s no question that some portion of the gift card business is incremental.

You start with the breakage - cards not ever redeemed.  If I give a gift card to someone who doesn’t use it - $8 billion in gift card sales last year are in this bucket - then as the retailer ISSUER (as opposed to the gift card seller) I have to be in the black on it, from an operational  perspective.  I can’t see any way there’s no incremental profit in that idea.  Incremental SALES, maybe not, but profits?  Have to be there, industry as a whole.

In case the above is not clear, the issuer would be where the card is redeemed.  Originally this point of clarification would not be required but as you know, now there are lots of stores that sell gift cards from other retailers!  Whether that kind of operation is incremental to the SELLER of the card is a merchandising issue, but I’m pretty sure it is incremental to the ISSUER - the store where the card is to be redeemed - based on the industry breakage.

So, if your client is a retailer, an ISSUER that sells their own cards, either through their own store or other stores, than I’m pretty sure there is incremental business there. If they are a seller of other store’s cards, or a processor of some kind, then I’m not sure.

But let’s say 100% of cards are redeemed.  Now it’s a little more tricky, you have to look at the incremental cost of the cards / processing versus the “float” on the money.  This is a pretty simple equation.  The costs are whatever premium may be charged for the creation / processing of the cards versus the interest on the money taken in from the sale of the cards.

For example, if the average “days to redemption” of a card is 90 days, and the average value of a card is $100, and the interest I can get on that $100 is 4%, then I make $1 ($100 x 4% / 1/4 of a year) for every $100 card I sell just on the “float”.  If the incremental costs (say, versus a regular credit card transaction) to issue and redeem this card is less than $1, then as the Issuing retailer I am making the difference as incremental profit - even if there is 100% redemption, which for sure is not the case.

Now, if the retailer is doing something else with these cards - using them as rewards, store of value, etc. - the story could be different.  For example, “buy $100 worth of merchandise and we will give you a $5 gift card” or “redeem your loyalty points for a gift card”.

That’s a different story, now the card is not a “product” it’s a transactional device / store of value and that changes the dynamics.  In this case, the card is no different than issuing coupons and you get into problems because these kinds of promotions tend to attract best customers, and their purchases using the card may not be incremental.

As far as incremental profit goes, now you stand a good chance of being in the hole, at least with the best customer segment.   And since frequently the volume of losses in this “best” segment for promotions like this will dwarf any gains from these promotions on any other segments, you end up in the hole with incremental profit.

Put another way, the best, most engaged customers are highly likely to purchase anyway and giving them a discount changes nothing about their spend, they simply buy the same amount at a discount.  This aggregate discount is usually greater than the aggregate incremental profit on not-so-best customers, so the entire promotion operates at a loss.

The good news is this: the actual incremental profit of programs like these is simpler to measure, because you have all the transactional data and you can control / put parameters around the issuance.  Typically in a scenario like this you would  set issuance rules that threshold above the average spend rate of the customer segment.

So, for example, let’s say the average monthly spend of a best customer is $80 or $960 a year. The correct promotion then looks like this: buy $100 worth of merchandise **this month** and we will give you a $5 gift card.  Hopefully, at minimum you would see a total spend of $980 that year - $20 more than average.  You would be on your way to incremental profits at this point, depending on what margins are in the business.

What you *do not* want to see is the customer spend $100 during the promo month and then spend $60 the month after, which is a typical thing that happens with best customers.  This is a sign you are not driving incremental sales, you are simply moving the existing spend around and giving up $5 in profit as you do it.  Not good.

For more on setting up these kinds of tests and measuring these types of effects, see this article.  Hope that gives you - and the rest of your industry - a starting place!

(Follow-up Question)

Q:  You have however validated my thinking that we can’t measure incrementality without creating control groups.  In asking the question, I was second guessing myself and wondering if there was something I was missing.  I think to some degree we can use existing sales data to measure uplift (either over the value of the card itself or over the client’s average transaction) for the current purpose which would move them to the next phase of specifically measuring incrementality.  For this, I’m sure your book will be helpful and I shall use it accordingly.

A:  Yes. Along those lines, in the “threshold” example, we have seen a $10 off $50 generate an average sale of $120.  Given the customer segment had an average purchase of $45 - hence the $10 off $50 - it would be safe to assume there’s some incremental in there, and long as the customer segment spend doesn’t tank by $70 or so over the next several months, you could assume pretty safely you have incremental profit.

“Proof” is another thing, but if you’re dealing with pure retail, proof is a matter of degree, you take what you can get.

(Follow-up Question)

Q:  P.S. Your point about the promotional use of cards, this company has separate business units which are their own P&L centers.  So, while the promotional use of cards might be incremental to the business unit which runs gift cards, it’s only incremental to the company to the degree that it produced new business or “stole” it from a competitor less than value of the gift cards it took to produce.  Again, greatly value your time and input.

A:  Yes, well, that’s quite another matter, and gift cards are not the “root cause”, if you know what I mean.  There is a long tradition of ”intra-company sales theft” between divisions that seemingly goes unchecked.  A lot of direct marketing companies are sensitive to this issue, but that’s because they can measure the effects,  Even then, there are only a few who have really unlocked the riddle of how much / where / when it happens, especially relative to web sites versus catalogs and retail stores. Segmentation of Recent customer buying patterns is the key.

Anyway, this is a “governance” question (strategic), not a marketing / gift card question (tactical).  If a company really wants to measure channibalization, they can by setting up specific tests and using control groups to measure outcomes.

The question is, do they really want to know the answer?

The following is from the December 2007 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here.

Q:  I have been asked to create a whitepaper on marketing strategy and tactics for a down or recessionary market.  In your studies and travels have you come across any literature or have thoughts of your own that I may quote?

A:  Well, I suppose someone has written something about it somewhere.  The trades write about it for every downturn! But I don’t know of any primary work on the topic - case studies, research, etc.

I do know that when we get into a down / recessionary market my phone rings more and I work a lot harder.  The “new client” customer retention business is counter-cyclical; people always wake up during the soft times and say,  “Hey, if we can’t drive new customer volume, maybe we can sell more to existing customers!”.  You know, the CEO or somebody read that somewhere…

The problem with this kind of thinking is, in most cases, it’s already too late to do anything about customer retention.  That’s not something people generally want to hear.  I then say, “The economy is cyclical.  Do you want to be prepared for the next downturn?”

The people who answer yes to that question will often become clients; those looking for the “quick fix” generally won’t become clients - but they call again into the next downturn…

It’s a strategy thing, you know?  Long term thinking?  But I digress…

The insidious thing about customer defection is that it’s always there, eroding the asset base, wasting away the hard work.  But people don’t see it until the flow of new customers shrinks, and then all of a sudden, the defection issue is laid bare. 

This is why the retention business is so counter-cyclical; why “discovery” comes in  the downturns.

What you normally find is whatever business change / policy / product is causing customer defection, it takes as long to build up the customer asset again as it did to destroy it. Here is a real-world example.

A retailer makes a significant change in the types of products it sells, because it wants to “attract more new customers”.  For existing customers, revenue per customer starts to fall.  This fact is masked on the revenue side by the attraction of new customers to the new products - for a while.  But it ends up these new customers, in terms of revenue per customer, have a value about 30% less than the old customers.  So even though new customer adds remain consistent, sales start to drop, and over time drop by 30% as old customers defect and are replaced by the new customers worth 30% less.

Two years into this process, a downturn in the economy causes more attention and analysis of the customer base, and this issue is exposed.  Surprise!  The newer kind of customers defect at a higher rate and in a shorter time than the old type of customers.

New management is brought in, and they decide to go back to selling more of the “older” product to attract the higher value customer.  Once they make the switch, it takes just as long for sales to get back to where they were as it did to create this problem in the first place - 2 (very long) years.

And that’s why it is so tough to deliver a “quick fix” to these kinds of problems.  They are systemic in nature and because you are talking about the value of a customer over time, take time to fix.

So, it may well be that your advice should ultimately be “use this downturn to prepare for the next one”, if you know what I mean.  Investigate, learn, and understand what happens this time, so you know what to do next time.  In terms of action items, a few:

1. Analyze the customer base, to understand the source of customer value.  Who are the best customers, where do they come from? Which media, sales persons, product lines, services, geographies, etc. create the “best  customers” for the business?

2. Analyze these best customers, and understand their behavior.  What would be a warning sign that these best customers - who are probably responsible for the lion’s share of your profits - are cracking into the downturn?  Slowdown in orders per month, average order size, number of contracts, whatever the relevant metrics are.

3. Track a handful of these customer metrics and see how they change as the economy slows.  These metrics will be a map for predicting actual trouble the next time - predicting trouble even before everyone is already talking about “a downturn”.  This gives you the extraordinary advantage of lead time over your competition in reacting to the downturn in business.

4. Complete the same 3 steps above for medium value customers and low value customers, if you have the resources.

5. Now, fully understanding what you have to work with (perhaps for the 1st time?), what is the strategy for a downturn?  Generally, it would consist of a reallocation of resources away from lower productivity to higher productivity activity, in order of importance:

a. For best customers, how do we keep them? 
b. For mid value customers, how do we grow them?
c. For low value customers, how do we reduce costs to acquire or service them?  Note I do not advocate “firing” customers, but you certainly can cut back on acquiring as many low value ones.

For each group, you should have a specific (and probably different) strategy and set of tactics.  What a lot of folks don’t understand is there is almost always a truly remarkable difference between these customer groups, and any “one size fits all” edict or direction is bound to screw up the business,  just like the example of the “new customer” effort from the retailer above.

For example, we know that marketing spend generally softens in a downturn.  Companies cut back on marketing because they feel like they are “pushing on a string”.  They cancel or don’t buy advertising, they fire salespeople.  This is the wrong move.  The old saw about buying more marketing into a downturn to “grab share” can also be the wrong move, though has some “accidental” positive effects.

The company should invest in more marketing, but not across the board.  They should buy the right marketing, the marketing that generates the best quality customers.

They should reallocate marketing resources away from generating “c” customers towards generating “a” customers.  If you know trade shows generate leads which turn into “a ” customers and online ads generate leads that turn into “c” customers, you take the money you spend online and book more trade shows.  You let go of salespeople that generate “c” customers and use that salary to bonus salespeople generating “a” customers.

Of course, this analysis and planning is an exercise that should be done all the time, not just into a downturn.  A business should always be trying to understand where customer value comes from and how it is created.  But unfortunately, this issue most often comes up going into a downturn.

You’ll have to excuse me now, the phone is ringing again…

Comments or questions?  Does your company have a “downturn plan”?  Or is it business as usual, just less marketing activity across all the channels?

The following is from the November 2007 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

 Q:  We are a manufacturer with a cool product not really on the net and right now, but we are entering stores.  I wonder, is it wise to try to sell on the net before retail markets have the products or not - is it better to hold off until the retail markets first have the products and then launch them onto the net?  Does the net really help sell products or does it create copy cats?  Trying to find the best way to go - any advice would be greatly appreciated.

A:  Great question.  Answer is “it depends” and it’s difficult to be more specific without knowing more about the product and your marketing plans.  But in general, if you want to optimize the profitability of the product launch and you are paying for media, you should think about these choices as a “chain” or series of events each with a specific but interconnected strategy for each channel.

An example would be DRTV or infomercial products, which generally are launched at a higher price into the spot TV channel (cable networks, etc.).  Here sales are made at a very high margin but the volume is generally low; the Objective is to generate awareness and hopefully make a profit, but breaking even is OK because you essentially have the media “free” and that will help drive the next step.

Based on all the awareness you have generated with TV spots, you then can go to the TV shopping channels and say, “Look, people know this product because we have already pre-sold it for you.  We will let you sell it at a lower price if you will drive volume”.  And that’s typically exactly what happens; most of the profit on the product is made here.

From the spot TV, the audience knows the product sells for $19.95 or whatever, so when it is offered at $14.95 on the shopping channel they think it’s a great deal and the volume is tremendous.  Typically, the spot TV would still be running at this stage, though sales from that channel will have peaked.

Once sales get soft in the TV shopping channel, you then introduce the product online and in stores.  This is essentially “end of lifecycle” for the product, where you are simply trying to make sure you don’t get stuck with any.  You sell that at cost plus to the onliners / retailers and they blow them out at $9.95 or so.  You don’t end up wearing the inventory and everybody is happy because the spot TV / TV shopping has generated plenty of awareness, people pounce on the product, and it moves very quickly through retail.  Typically no TV would be running at this stage because you couldn’t sell any at the original price.

Now, I’m not saying you should follow this model.  But what I am saying is the decision you are trying to make is more complex than “should we”, it involves understanding which channel can do what for you and at what price.

For example:

You said you are “entering stores”, but did not say if you / the stores are running any media to support this effort.  If you are not running any media then I would get on the web and sell the product for retail price or higher.  This generates some awareness / demand / trial but preserves the margins of the retail partner, and hopefully your direct profits will cover costs.  You basically get “free media” from the web (as in the spot TV example above) and the retail folks will love it because it will drive sales in their channel.

If you / the retailers are doing a lot of paid media support, then I would not sell on the web until sales through retail get soft.  Then you are in a position to undersell them or liquidate on the web based on the awareness you have generated offline.  This doesn’t mean you should not have a web site, you should, and it should tell people which retail outlets they can buy the product in.

On the other hand, if there is a razor / razor blade model built into the product (think a doll with add-on sets of clothing), you could sell the primary razor product and some of the blades in retail, then develop more targeted / segmented / rare blade offerings that are exclusive to the web for online stores.

Again, it’s very difficult to make the “right” judgment on this question not knowing anything at all about the product, whether there are supplemental / follow-on products, whether there are continuity pieces involved (collections) and so forth; and especially not knowing what the nature of the retail relationship is.

But I think you get the general idea.  You play the strengths of the channels off each other, generally in some sequential way, depending on what the marketing / media plan is and the characteristics of the product.  That is, if you are interested in optimizing media spend versus sales.  If you have an unlimited media / PR budget, then sure, sell it everywhere!

Hope that helps.

===============

Comments?  Questions?  Better ideas?

The following is from the October 2007 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

Q:  I ordered your book and have been looking at it as I have a client who wants me to do some RFM reporting for them.

A:  Well, thanks for that!

Q:  They are an online shoe shop who sends out cataloges via the mail as well at present.  They have order history going back to 2005 for clients and believe that by doing a RFM analysis they can work out which customers are dead and Should be dropped etc.  I understand Recency and have done this.

A:  OK, that’s a great start…

Q:  But on frequency there appears to be lots of conflicting information - one book I read says you should do it over a time period as an average and others do it over the entire lifecycle of a client.

A:  You can do it either way, the ultimate answer is of course to test both ways and see which works better for this client.

Q:  Based on the client base and that the catalogues are seasonal my client reckons a client may decide to make a purchase decision every 6 months.  My client is concerned that if I go by total purchases , some one who was  really buying lots say two years ago but now  buys nothing could appear high up the frequency compared to a newer buyer who has bought a few pairs, who would actually be a better client as they’re more Recent?  Do I make sense or am I totally wrong?

A:  Absolutely make sense.  If you are scoring with RFM though, since the “R” is first, that means in the case above, the “newer buyer who has bought a few pairs” customer will get a higher score than the “buying lots say two years ago but now buys nothing” customer.

So in terms of score, RFM self-adjusts for this case. The “Recent average” modification you are talking about just makes this adjustment more severe.  Other than testing whether the  “Recent average” or “Lifetime” Frequency method is better for this client, let’s think about it for a minute and see what we get.

The Recent average Frequency approach basically enhances the Recency component of the RFM model by downgrading Frequency behavior out further in the past.  Given the model already has a strong Recency component, this “flattens” the model and makes it more of a “sure thing” - the more Recent folks get yet even higher scores.  

What you trade off for this emphasis on more recent customers is the chance to reactivate lapsed Best customers who could purchase if approached.  In other words, the “LifeTime Frequency” version is a bit riskier, but it also has more long-term financial reward.  Follow?

So then we think about the customer.  It sounds like the “make a purchase decision every 6 months” idea is a guess as opposed to analysis.  You could go to the database and get an answer to this question - what is the average time between purchases (Latency), say for heavy, medium, and light buyers?  That would give you some idea of a Recency  threshold for each group, where to mail customers lapsed longer than this threshold gets increasingly risky, and you could use this threshold to choose parameters for your period of time for Frequency analysis.

Also, we have the fact these buyers are (I’m guessing) primarily online generated.  This means they probably have shorter LifeCycles than catalog-generated buyers, which would argue for downplaying Frequency that occurred before the average threshold found above and elevating Recency.

So here is what I would do.  Given the client is already pre-disposed to the “Recent Frequency” filter on the RFM model, that this filter will generally lower financial risk, and that these buyers were online generated, go with  the filter for your scoring.

Then, after the scoring, if you find you will in fact exclude High Frequency / non-Recent buyers, take the best of that excluded group - Highest Frequency / Most Recent - and drop them a test mailing to make sure fiddling with  the RFM model / filtering this way isn’t leaving money on the table.

If possible, you might check this lapsed Frequent group before mailing for reasons why they stopped buying - is there a common category or manufacturer purchased, did they have service problems, etc. - to further refine list and creative.  Keep the segment small but load it up if you can, throw “the book” at them - Free shipping, etc.  

And see what happens.  If you get minimal  response, then you know they’re dead.

The bottom line is this: all models are general statements about behavior that benefit from being tweaked based on knowledge of the target groups.  That’s why there are so many “versions” of RFM out there - people twist and  adopt the basic model to fit known traits in the target populations, or to better fit their business model.

Since it’s early in the game for you folks and due to the online nature of the customer generation, it’s worth being cautious.  At the same time, you want to make sure you don’t leave any knowledge (or money!) on the table.  So you drop a little test to the “Distant Frequents” that is “loaded” up / precisely targeted and if you get nothing, then you have your answer as to which version of the model is likely to work better.

Short story: I could not convince management at Home Shopping Network that a certain customer segment they were wasting a lot of resources on - namely brand name buyers of small electronics like radar detectors - was really worth very little to the company.  So I came up with an (unapproved) test that would cost very little money but prove the point. 

I took a small random sample of these folks and sent them a $100 coupon - no restrictions, good on anything. I kept the quantity down so if redemption was huge, I would not cause major financial damage.

With this coupon, the population could buy any of about 50% of the items we showed on the network completely free, except for shipping and handling.

Not one response.

End of management discussion on value of this segment.

If you can, drop a small test out to those Distant Frequents and see what you get.  They might surprise you…

Good luck!

Jim

The following is from the August 2007 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

PRIZM Clusters Not as Predictive as Behavior

Q:  I am on an interesting project (and my first DB Mktg one): the client has a large loyalty program, and loves his PRIZM clusters.  However, when I told him a little more about Recency and suggest that we spread all members across based on it, he was surprised to see that his PRIZM segments were not a predictive indicator at all!

A:  Yes, and here is something many people don’t realize about PRIZM and other geo-demo programs, including census-driven.  They were developed for site location - where should I put my Burger King, where should I put my mall? They are incredibly useful for this.  However, think about all the sample size discussions for web analytics in the Yahoo Web Analytics Group related to A/B testing, and now imagine what your PRIZM cluster looks like.

In most cases, you are talking about 1 or maybe 2 records in a geo location - what is the likelihood these households reflect the overall “label” of the PRIZM cluster?  Combine this with the fact that for customer analysis, demographics are generally descriptive or suggestive but not nearly as predictive as behavior and you have a bit of a mess.

Here’s a test for you.  It only requires rough knowledge of your neighbors, so should not be very difficult (for most people!)

1.  What is your “demographic”?
2.  If you were to walk around the block and knock on doors, how many households would you find that are “in your demographic”?

Right.  Maybe a handful, unless you live in a brand new housing development or other special situation.  Now think about walking your zip code, or walking out 10 blocks or so from your house in any direction, and knocking on doors.  Do you find most of these people are in the same demographic as you are?  Did you ever find the “cluster average” neighbor?

We certainly know from web analytics that dealing with “averages” can be very dangerous indeed.  So too with taking a demographic “average” of a zip or other area and tying it to a specific household.  The model falls apart at the household level of granularity.

So now what to you think of all those websites and services that claim to know demographics based on a zip code they captured?

Now, if you think about an e-commerce database, with most records being one of a very few in a zip or cluster, you can see how the cluster demos would really break down at the household level.

Again, nothing wrong with using these geo-demo programs for what they were intended to be used for.  When you are looking for a mall location or doing urban planning they can be very helpful.  But the match rates at the individual household level are poor.

Couple this with the fact that e-commerce folks are usually looking for behavior from customers, and the fact demographics are not generally predictive of behavior by themselves, and you have yourself analytical stew.

Better than nothing?  Absolutely, and for customer acquisition, sometimes all you can get.  Best you can be?  Not if you have the behavioral records of customers.  In fact, what we often see is a skew in the demographics being called “predictive” when the underlying behaviorals are driving action.

In other words, let’s say a series of campaigns generates buyers with a particular demo skew.  A high percentage of these Recent responders then respond to the next promotion.  If you look just at the demos, you would see a trend and declare the demos are “predictive” of response, even though they are incidental to the underlying Recency behavior.

I suspect something like this was going on with your client.  Not looking at behavior, over time the client becomes convinced that the PRIZM clusters are predictive, when for some reason they are simply coincident in a way with the greater power of the behavioral metrics.  Given the client has behavioral data, that should be the first line of segmentation.

Q:  After reading you for some years, I now understand how one must be very careful with psycho-demographics.

A:  Well, at least one person is listening!  And now you have seen how this works right before your very own eyes.

I think this situation is really a function of Marketers in general being “brought up” in the world of branding / customer acquisition.  Most Marketers come up through the ranks “buying media” or some other marketing activity that focuses on demographics to describe the customer.  And most of the college courses and reading material available focus on this function, so even the IT-oriented folks in online marketing end up learning that demographics are really important.  And they can be, when you don’t know anything about your target.

Then the world flips upside down on you, and now people are looking at customer marketing, and that’s a whole different ballgame.  The desired outcome is “action” that can be measured and the “individual” is the source of that outcome, as opposed to “impressions” and “audience”.  

In the past, if your tried and true weapon of choice for targeting was  demographics, that is what you reach for as you enter into the customer marketing battle.  Problem is, it’s just not the best weapon for that particular marketing engagement.

The following is from the May 2007 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

Tracking UnTrackable Campaigns

(Any Tracking is Better than No Tracking at All)

Q: I have a background in direct marketing and the measurement of campaigns using defined sources.  Now I am working at a technical or 2 year college and I’m trying to use my previous knowledge and experience to measure our return on marketing efforts in recruiting new students and converting them from prospects to enrolled students at the school.

I’m looking to measure events and all of the advertising and promotion used to communicate those events as well as other campaigns.  It’s difficult to measure since it may include newspaper ads, direct mail, posters, etc.  - not just direct mail with a measurable list to refer to for tracking response.

Plus, trying to get the admissions folks to track leads can be difficult.  They also want to track return on publications (brochures, flyers, etc.)….not sure how one would do that if they are not mailed to a given measurable list?

I’m looking to track ROI based on the whole equation….from the # of inquiries who came to an event or responded to whatever all the way to whether they matriculated and enrolled in courses.

A:  I can feel your pain!

I don’t think there are any easy answers to this.  You could simply measure what you can measure through the traditional direct methods you are familiar with, and let the rest “ride”.  Or, you can try to selectively determine, as best you can, what the value of all these other activities is by engaging in some kind of testing.  This will take some institutional willpower and is probably something you can’t do on your own.  In other words, I encourage you to start evangelizing the next generation of marketing measurement at the school.

From your title, I perceive you don’t “report to” marketing, but some higher institutional level responsible for Quality and perhaps Productivity / Accountability for funds that are spent.  It sounds to me like your unit might perhaps report into the Financial area of the school at a higher level, and if so, that’s good!

I think I would simply start the conversation with folks in the financial area about some of these issues, and see if you can create some simple tests to get some “direction” on the contribution of the various marketing outlets.

For example, every publication should contain some kind of tracking device.  Sometimes you have to be creative with this idea and it won’t always be accurate, but it’s better than nothing at all.  If response is generally by phone, then try to get a unique phone number for newspaper ads, brochures, etc.  If response is driven to the web site, get unique URL’s put on each document.  If response is filling out a sheet or card at an event, have them numbered or coded in some way.  Then of course, you need to get the response information - number of phone calls to each number, number of visits to each unique web site URL, number of response cards mailed or turned in.

Implementing a program like this, then finding and getting access to the response info may not be easy, and that’s why it would help to have a “higher power”, particularly a financial one, backing this effort.  It’s pretty amazing what people will do when, for example, the people who control the budget for an area say, “You will participate in this tracking program”.

Alternatively, you could go with a test / control kind of scenario where during a quarter, you leave out one particular marketing effort and see if there is an impact on overall Marketing Productivity.  This is more of a “marketing mix” kind of approach and not without some problems, including proving the missing marketing effort was responsible or not for changes in Productivity.  You have to think about how you might pin these issues down in advance - for example, do you have good baselines for “normal” activity?

Whether or not you decide to pursue all of this is somewhat of a personal choice.  Some analysts simply don’t think it is their “job” to help create measurable structures - they only measure what can be measured.  Others see the difficulty as a challenge, and want to help build out the structure.  Clearly, if you are going to eventually be responsible for measurement, being a part of the team constructing the measurement paths is a real advantage to you.  It will involve some politics, but analytics always involves politics at some level.  I encourage you to seek out the support you need to make this work.

If there is “pressure” for measurement, someone wants it to happen.  Start by finding these people and having a conversation about how it could happen, the strengths and weaknesses of the measurements, the internal challenges you will face.

When taking on something like this, it’s usually best not to try to change the world all at once, but one step at a time.  So, for example, looking at the overall “unmeasurable budget”, what is the largest line item?  If it’s “newspaper”, that’s a place you probably have the largest leverage.  Implement there first, keeping in mind that this single implementation might help you down the road.  For example, getting a unique phone number and results tracking for newspaper may teach you a lot about how to get this done for other marketing devices.

The finance people should be able to provide you with some idea of the net “margin” of a course and any other financial ideas that come into play.  Then it’s a matter of asking if the spend on the media generated positive results.  If the margin on a course is $500, a newspaper ad costing $1000 that only generates 1 student is not a great investment - but it might be the best one relative to other vehicles.  This part is not really your call.  Your job is to bring the data to life so that people can understand what they are spending and what they are getting.

There could be plenty of reasons why “losing $500″ on a newspaper ad is OK - there is “brand exposure”, for example.  In this case, the brand exposure only costs $500 versus a perception that it costs $1000, if the student generated is included in the formula.  This may be a very positive result of the measurement for many folks in the institution.  That judgment is really for someone else to make.  Now at least they are making it on a full set of facts as opposed to perceptions they have about cost.

Q: However, how long do you keep measuring enrollments?…they may not enroll based on one campaign…might take a few hits before they actually become students.

A: Sure.  What seems reasonable?  Given an annual budget cycle, let’s say reasonable is 12 months.  One benefit from your tracking is you will be able to probably put some numbers against this eventually.  If you get calls to a brochure number 2 years after it was issued, then the number is 2 years for a brochure.  Newspaper calls stop coming in at 4 weeks, it’s 4 weeks for newspaper.

Q: The other issue: the marketing folks only want to measure up to inquiries–what they have control over.  What’s the best way to only measure the return on that…it’s before a “sale” or “enrollment” even occurs, so the “profit” is not booked.

A: Well, sometimes you simply have to decide what is “best available”.  You certainly can start by measuring inquiries, especially since it’s pretty clear in this case marketing lacks some control over key conversion elements - financial aid, student abilities, and so forth.  Down the road, it’s possible that certain types of media generate lower quality inquiries with lower conversion rates.  You will get there over time.

For now, you could apply the “average conversion” to any lead to get down to the financial part of the game.  If all leads on average convert at 25%, then just use that.  Then when tracking gets a wider reach, try to drill into it more deeply.  To do this, you’d have to get access to enrollment data, of course.  But you don’t have to get “all the data, all the time”.  You could do a sample of a couple of months and go through it by hand to match back to inquiries, if you have to.  You certainly would not be the first to do something like this to pin down an issue - it happens all the time.

Whether you want to do something like a “by hand count” or see it as part of your job is really more of a personal choice.  You can certainly - and analysts often do - blame a lack of knowledge on system problems, politics, whatever.  Just can’t get the data.  For some people “don’t know” is not acceptable - they have to find the answer, whatever way they have to do it - even if it is by hand!

Q: Does your book give an education example?  We’re not “selling” a product and “sales” deals with # of credits taken by students & price per credit plus funding we receive from the state gov’t based on the number of FTEs generated.  It seems like such a different animal so I’m struggling to figure how to do ROI for an educational services provider.  I have created an Excel template based on how I calculated ROI for other industries but haven’t tried it yet.

A: Sounds like the “profit” side of it is a bit complex with the outside funding, but I’m sure you can get to a “value per FTE” somehow.  Just start with something, and make it better as you move along.  If you have to, simply take “all revenue” divided by number of FTE’s and you at least have a place to start.

The book doesn’t have extensive educational examples but here are some related topics from the newsletter:

Predicting Student Churn

Profiling Library Customers

I have a lot of interest in these educational measurement scenarios for a variety of reasons.  Keep me posted on how you are getting along and ask any questions you might have as you make your way through.  If I can be of help let me know!

Jim

Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Perhaps a question about Marketing Productivity or Web Analytics?  Just ask your question.  Or, feel free to leave a comment about this post. 

The following is from the April 2007 Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

6033% ROI
—————-

Q: We have exchanged email a few times, and I don’t recall if I ever said thank you for your book.  While I had been experimenting with many CRM programs in my little dry cleaning shop, your book gave my thoughts order and clarity to refine what I had started.  Today, I see the world differently.

A:  Well, thanks for the thanks!

Q: You may or may not remember me.  Just after I sold my dry cleaning shop, I had bought your Drilling Down book.  I was the dry cleaner who had been doing rudimentary data mining and CRM with a point of sale system I had developed in Regina, Saskatchewan.

A: I do remember.  Internally, I was thinking, “Wow, this is going to be a real test of the Drilling Down concept”.  I mean, I have seen it work in many small businesses, but dry clean (seems to me) is a very tough, tough business.  Too many players, a lot of competing on price, etc.  A great environment for underground customer marketing in terms of beating the other guy - they will never know what happened to them.  But still, tough for small owner / operator to have the “will” and time to really make it happen.  So yea, I remember…

Q: Well, I’ve continued working within the dry cleaning as a marketing consultant.  The programs I had developed in my shop have now been transplanted into a few of my client’s shops, and are bearing fruit.

Tonight one of my clients reported a ROI of 6033% doing direct mail to certain customers in his market in California.  Another client of mine reported his fourth year of steady growth.  One of my first clients has been showing a 7 percent annual compound growth, and he is in a flat or declining market.  What began in my shop has been proven across North America, into Europe and Australia by my clients.

A: I can’t express how exciting that is.  Congratulations!

Q: Jim, data mining dry cleaner’s data is a blast.  You would be stunned at the quantity, and quality of data a dry cleaner gathers today.  Would you ever have thought data mining could be applied to suits and shirts?  Well yes, it can.

A:  I am stunned, and I bow to your most excellent Drilling!

Q:  Once again, thank you.

A: And thank you for sharing this, it’s very, very exciting to hear.  Like you said, no other word for it than “stunning”.  I remain most stunned!  Keep me informed.  Perhaps you should write a book?

Jim

Reader P.S.  If you own a service business that depends on repeat customers and would like to contact the fellow Driller responsible for producing the results above, let me know.  I’ll send you her e-mail address.  I can tell you these are not the only success stories she has to share with you…

Defining Churn
———————

Q: I work for an economics consulting firm based in Washington DC.  I am researching customer churn and customer displacement statistics across a variety of industries to try to establish a benchmark of what is considered high and low customer displacement.

A:  Nice to meet you, and a noble task!

Q: Do you happen to have any such churn statistics, or know if a place you could recommend?  I found plenty of statistics regarding churn rates within the telecom industry, but am most interested in companies that are involved in business-to-business relationships with their customers (relationship between a customer and a supplier).

In addition, I would also like to find churn statistics for customers who use multiple suppliers.  For example, a customer may go to several grocery stores rather than sticking with one dedicated store.  I would be interested in learning more about the statistics companies in these types of industries use to track customer displacement.

A: The reason you find a lot of churn info in telco / cable is the end of the customer life is easily defined by the disconnect, and these numbers are reported publicly as part of annual reports and so forth.  In many other businesses like the ones you describe, typically the companies have failed to define customer defection and so in their minds, there is no churn because there is no defection.

A “customer”, even though they have not contacted the company for 3, 5 or 10 years, is always still a customer.  If the company thinks like this there is no churn rate to be measured, by the definition the company has chosen for itself.

At the same time, defining defection is pretty easy to do by looking at the transactional data and defining the patterns of defection, for example “if a customer has not ordered from us in 3 years they are highly unlikely to order again”.  That’s defection defined; you just put a line in the sand and say “3 years no contact is a defection”.  The company then should declare customers in this status “defected” and then a churn rate could be found.  This is pretty easy to do, so if not executed, one of two situations exist: either the company does not have the data or they don’t have the “will” to discuss, internally or externally, the concept of customer defection.

A third possibility exists: the company in fact has the data and has defined defection, but would never, ever speak to churn or customer defection in any kind of public forum because this information is so critically important from a competitive and strategy perspective.  To discuss these numbers or the implications in public could have dramatic consequences for company positioning in the market or stock price.  So if they have the numbers, they’re locked in a safe.

As a result, I’m sorry to say, I do not have any broad-based “sources” for you, save one possibility: a book called The Loyalty Effect by Frederick F.  Reichheld (1996).  In this book, Reichheld goes through the business models of 25 different companies that excel at retaining customers in different industries , and proves out the financial model of customer retention using real data.  This is the book where the quote, “It costs 5x more to acquire a new customer than retain a current customer” (or the various bastardizations) came from.  So it might help you out.

The only other thing I can suggest is that “churn” is not always the word used to describe these stats but is most often used when the disconnect is easily defined, as in telco / cable; “displacement” is a rare use for this idea as far as I can tell..

“Customer Turnover” is a popular phrase in Europe and is used by some in the US; also “defection rate” is used quite a bit.  So if you’re pounding on Google to try to find these numbers, try those phrases and others you may find when doing these searches.  Banking / finance / insurance is another area where the “disconnect” is often easily defined, so you will find various defection rates in some of their case studies on the web.

Jim

The following is from the March 2007 Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

Optimizing Mail Drops for Consumables

Please note: The business discussed below is a “continuity business”, where customers consume the product and need to either reorder from the company every few months or seek alternatives sources for the product.  In this scenario, the behavior of customers is generally governed by the Latency Metric.

Q: Currently we mail our current customers direct mail every 6.5 weeks.  We have a new VP and he is asking if that is the optimal spacing of mailings.  I’m wondering if there are any best practices for setting up frequency tests?  If you can shed any light on how to set up such a test I would greatly appreciate it.

A: Well, do you know how you got to the 6.5 weeks number in the first place?  Somebody must have thought it was a good idea based on some kind of data (I hope)!

Obviously, there is some significant financial risk in simply “moving the drop around” and testing results that way.  You can do it, often by slivering off parts of the drop and dropping then at different times, but there could be a substantial financial penalty for approaching the problem this way - both on the cost and sales sides.  This is especially true when you have a current schedule that seems to be working.

The first thing I would do, if possible, before taking on the risk of messing with the mailing is to see if you can find any segmentation /  frequency that makes more sense from the customer data itself.  Since you also have a web site, there probably is evidence of “natural purchase cycles” the customer engages in that operate outside the mail drop - customers ordering “when and how they want to”.

Can you find evidence that the average purchase cycle is more like 5 weeks or 7 weeks?  How does this differ by product line, or packaging of the product?  Both segmentation by actual customer behavior and segmentation by product line will generally provide increased profits, provided the cost of dropping different mail streams does not overpower the increased sales.

For example, if someone can buy a “90-day supply”, well, 6.5 weeks is a bit  early for the mailing, I’d think.  If they can only buy a 30-day supply, well, it seems to me that 6.5 weeks could be a bit late.  Look to actual  purchase cycles by product line / supply length and see if you can find any patterns in the purchase behavior.

The key to this kind of analysis is to line up all the customers so that the purchase cycles match.  In other words, you need to enforce the same start date.  One way to do this, for example, is look at all new customers who started in January 2007; of the ones that bought again, when did they purchase - 5 weeks, 6 weeks, 7 weeks out?  What percentage of new starts in January (or any other month) purchased in each of the subsequent weeks?  Be aware choosing a single month may create results that have a seasonal bias, but I’m not sure that is relevant in a product line like yours.

A more complex but possibly more accurate way to do this is to “normalize” the start date of all new customers in 2006 and then look at the subsequent purchase patterns - given the same start date, what percent bought again 5 weeks out, 6 weeks out, 7 weeks out?  You can achieve virtually the same thing by taking each month of 2006 and running it through the same drill as the one described above for  January 2007, though it won’t be as accurate.

Once you have nailed the cycle for new customers, you can move on to see if  there is any change in optimal cycle date as customers age.  My guess is the cycle probably gradually lengthens until the customer defects.  If this is  true, it might be worth it to do two mailings with different cycles - one cycle for customers who became new customers in the past (say) 6 months and all other customers.  It’s likely in this business there could be an important behavioral difference between new and current customers that would allow you to deliver a more optimized mailing cycle.

Failing access to any analytical means to drill down into the data first, because either you lack the resources or simply don’t have the time, set up your next drop with flagged segments based on “weeks since last purchase” and look at profit per customer.  You could also back into this if you have good promotional history on your customers.

In other words, if you are going to drop “everybody” at the same time, there must be a segment where for this single drop, the time since last purchase based on arrival of the mail is 5 weeks ago, 6 weeks ago, 7 weeks ago, and so forth.  If you flag these segments before the drop in the database, you should be able to go back and determine sales per customer mailed for each segment.  This will tell you if your timing should be adjusted.  Further, you might divide these time-based segments, if there are enough members in the  segment, along various product lines.

Then, once you have a handle on the general cyclicality of different segments, you can get to profit per segment by using control groups to measure the lift and profit by segment.

A careful analysis of the next drop (or as I said, a previous drop if you have good history) should tell you which drop cycle for each product line is optimal.  From there, you have to look at economies of scale and decide if  you can afford that kind of segmentation.  You may find that due to the economies of scale in the mailing, you simply cannot drop 50% of your mail one week and the other 50% the next, for example.  But you might find enough support in your analysis to either justify the current 6.5 week drop as the most efficient, or to move it up or back somewhat.

Another way to approach the “timing problem” relative to economies of scale would be to try “reminder to re-order postcards” instead of mail or catalogs to some members of the group that require special timing considerations.  For example, new customers might not really need a catalog on their first drop, a postcard driving them to the phone or web site to reorder might be enough.

No silver bullets, I’m afraid. Just good ‘ol fashioned sloggin’ through the data ought to get you to where you want to go!

Jim

Robert, the questioner in this case, just checked in with an update.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask me.  Want to see the answers to previous questions?  The pre-blog archives are here.

————-

Jim,

Thanks for bringing my marketing inability to a wider audence!!  I’d be interested in responses you get from people doing similar work.

Just to keep you up to date on this project, we have had an amazing amount of interest from library senior management, local managers and library assistants based on my preliminary work with the data based on your methods. Interesting to see a team that is setting up / running a corporate call centre and CRM system have found out about this work and want to talk me about it - especially interesting as I work in a completely different part of the organization.

The only problem we have now is getting the data we require from our IT department. Hopefully this will be coming this month, and if you don’t mind, I’m sure I’ll be contacting you for further advice.

————

Hmm, there’s that IT thing again…Keep us in the loop, Robert!

The following is from the February 2007 Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Want to see the answers to previous questions?  The pre-blog archives are here.

Profiling Library Customers

Q: I work for a local council in England and was recently asked to provide some local demographic profiles to help our local libraries market themselves more effectively and hopefully increase book loans.

A:  Hmmm…this is definitely the first time for this question!

Q: I’m no marketer, I usually muck about with crime, economic and census data, but this seemed instinctively wrong to me after reading how Tesco’s had used its clubcard data to understand it’s market.  When looking for some help, I’ve obviously found your website, bought the book, got the software.

A: I would have to agree with you on the Tesco point.  If you want people to “do something”, you look at behavior.  If you want people to take out more books, you look at book loan behavior.

Q: Firstly, I just wanted to thank you for making your ideas so accessible, actionable and easy to understand.  I’ve picked up some other marketing textbooks for help and they seem to mainly consist of dry schematic diagrams, and bland statements.  Great for a degree I’m sure, not so great for the rest of us who have three weeks to write a report on the subject!

A: Well, thanks for the kind words.  That was the intent of the book - to give people the “how to do” as opposed to the “what to do”.

Q: Secondly, have you any advice or experience of this model working in the non-profit sector or specifically in libraries.  For example, predicting life cycle / trigger points seems a little more complicated than the examples you use.  People don’t seem to stop using the service gradually, but stop abruptly and then start up again without no warning.  I’m also dealing with thousands of records.  My data seems a lot less clear cut than the examples you talk about.

A: I’d agree it’s not a “clear cut” situation, but not because of the models or the channel.  This kind of behavioral profiling as been used offline for decades, and it works in all kinds of situations.  For example, you mention crime data, so you have probably seen that the more Recently someone has committed a crime, the more likely they are to commit another.  Not that you can really take any action on that information - you can’t lock people up for being “likely to commit a crime” - but interesting just the same.

And I think you face a similar challenge.  You can run the scoring models and generally predict who is likely to slow or defect from their book loan behavior, but the question is, what do you “do” about that?  I have worked in other educational situations (likely to graduate, likely to contribute to the school after graduation) where the incentive is not straightforward but nonetheless you can create incentives to encourage people to continue their behavior.  I’m struggling a bit with how to create one in this situation, since the product itself is free.

But before we tackle that issue, I want to run through a bit of a “model” for this “business”.  It seems to me you have a market or segment shift going on.  If the primary reason people go to the library is to research a topic, clearly access to the Internet has suppressed the need for people to take books out on loan from the library.  For example, many of the trade journals that used to be hard to access or expensive to subscribe to are now available on the web.  So you have this “research” segment you have to deal with.

There no doubt is another segment, “core readers”, who simply for the love of reading visit the library to discover books and read them.  This segment is probably what I would call “good customers” because the library provides a service to them they cannot get elsewhere, the “value proposition” of the loan program matches their needs precisely.  This in contrast to the “research reader”, who now can do a lot of research from work or home on the web.

For research readers, a possible alternative would be providing access to internet terminals in the library.  But now we’re starting to encounter a different definition of “customer” and “loan a book”, right?  Let’s say, for a library, the “profit” in the venture is the “contribution to the community”, and this contribution was always measured in the past by “books out on loan”.  This metric has been the library’s KPI (Key Performance Indicator), if you will.

Let’s also say that many libraries have installed internet access terminals, as they have in the US.  Because of these terminals, you would expect that “books out on loan” would fall for the “research readers” segment, correct?  So to get a proper valuation of the contribution the library was making to the community, you would have to look at “books on loan + number of web terminal uses” to approximate the old metric “books out on loan”.

Follow?  So let’s say the real issue at hand here is the local government is trying to be “accountable” for what they spend on libraries, and they measure the “profit” of this spending by looking at books out on loan.  The library administrators are feeling some kind of pressure to “serve the community better” (increase “profit”) because books out on loan have fallen.  The problem is that “books out on loan” is no longer a viable metric - the “base” has changed, if you will.  The research segment is being served through a new method - web terminals - and this has been overlooked in terms of measuring the “contribution” the library is making.

In terms of tracking, if there is no login required to use a web terminal, someone in the library simply needs to count the number of people in a week that use the terminals x 52 weeks and use that as an approximation.  Better would be a system where in order to access a terminal, you have to enter your “library card number” or some other unique identifier tied to the person or household.

In this way, you find out more about your segments:

Researchers = only logs into computer
Core Readers = never log into computer
Multi = both logs into computer and takes books out on loan.

These “multi’s” would typically be the very best customers, since they are engaging in more than one library offering.

Above this, the library probably offers other types of services and special events.  The more of these services and events a customer engages in, the more valuable the customer is.  These are the customers the library should strive to keep active.  And like the example of books on loan, if the “value” of the library is only being evaluated on books on loan, attendance at these other services and events really should be included in the evaluation in some way.

The landscape has changed, and it’s quite possible that the metrics have not kept pace.  Perhaps it is not your place to suggest this, but as the “evaluator”, I would certainly be curious about this metric “books on loan” and make sure it accurately reflects what people think it does and is serving the administrators in the way they think it is.

Now that we have a feeling for what the background might be, you still have the issue of what do you “do” about customers who appear to be defecting?  As I said, the behavioral models will give you this information, but then what can be done with this information, especially given what are probably fairly strict budget constraints?  It’s not like you can send them a discount on their next book loan!

In situations like this, I think the best alternative might be survey work.  That is, when the behavioral models identify customers who are likely to defect or have defected, the library simply asks them about it.  For example, the library conducts a telephone survey using a sample of these people - “We noticed you have not taken a book out on loan / used a terminal in the past 3 months, is there anything we have done to offend you?  Is there a particular kind of content you are now interested in that we do not provide?  What other events or services would you like to see us provide?”  etc.

And where possible, the library should respond and provide what these customers want.  It may not be able to keep these particular customers from defecting (too late), but over time the “mix” of content and services should improve in a way that attracts and retains high value library customers.

I humbly suggest that the above approach will be far more effective and less costly than a “CRM system”.  If the library doesn’t have one, what is really needed is a “tracking system” that simply keeps track of what resources of the library each customer is using.  This will make your models much more reflective of the true economic benefits provided by the library, and give you the customer samples you need to construct effective, segment-targeted retention programs.

This kind of work also provides very good tracking for “how we’re doing” as a library and can provide excellent justifications for budgets and new requests, because it is directly tied to what customers want - no guessing games.  Over time, budget should flow to the areas more desired by customers and away from areas that are perhaps less desirable or are “pet projects” of interested parties.

Q: Once again thanks, I can at least begin to show management that we should start doing this work in Access / Excel before spending on a CRM system, but any further advice would be appreciated.

A: Well, I don’t know much about how libraries really work, but I’ve given you my best guess as to what might be going on.  Hope that helps!  And do keep in touch on this as you get into the guts of it, should be a very interesting project!

Jim

Update:
Robert just checked in with new info on progress on this idea, see here.